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dc.contributor.authorBuchala, S.
dc.contributor.authorDavey, N.
dc.contributor.authorGale, T.M.
dc.contributor.authorFrank, R.
dc.date.accessioned2007-09-05T11:23:59Z
dc.date.available2007-09-05T11:23:59Z
dc.date.issued2005-11
dc.identifier.citationBuchala , S , Davey , N , Gale , T M & Frank , R 2005 , ' Analysis of Linear and Nonlinear dimensionality Reduction Methods for Gender Classification of Face Images ' , International Journal of Systems Science , vol. 36 , no. 14 , pp. 931-942 . https://doi.org/10.1080/00207720500381573
dc.identifier.issn0020-7721
dc.identifier.otherPURE: 92089
dc.identifier.otherPURE UUID: fda6d84c-2a1a-4c49-8c86-dfbe5284d15e
dc.identifier.otherdspace: 2299/597
dc.identifier.otherScopus: 31444438510
dc.identifier.urihttp://hdl.handle.net/2299/597
dc.descriptionOriginal article can be found at: http://www.informaworld.com/smpp/title~content=t713697751--Copyright Informa / Taylor and Francis Group. DOI : 10.1080/00207720500381573
dc.description.abstractData in many real world applications are high dimensional and learning algorithms like neural networks may have problems in handling high dimensional data. However, the Intrinsic Dimension is often much less than the original dimension of the data. Here, we use fractal based methods to estimate the Intrinsic Dimension and show that a nonlinear projection method called Curvilinear Component Analysis can effectively reduce the original dimension to the Intrinsic Dimension. We apply this approach for dimensionality reduction of the face images data and use neural network classifiers for Gender Classification.en
dc.language.isoeng
dc.relation.ispartofInternational Journal of Systems Science
dc.titleAnalysis of Linear and Nonlinear dimensionality Reduction Methods for Gender Classification of Face Imagesen
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
rioxxterms.versionofrecordhttps://doi.org/10.1080/00207720500381573
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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